Overview

Dataset statistics

Number of variables18
Number of observations403776
Missing cells71286
Missing cells (%)1.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory55.5 MiB
Average record size in memory144.0 B

Variable types

Numeric15
Categorical3

Warnings

REF_NO is highly correlated with yearHigh correlation
year is highly correlated with REF_NOHigh correlation
PM2.5 is highly correlated with PM10 and 2 other fieldsHigh correlation
PM10 is highly correlated with PM2.5 and 2 other fieldsHigh correlation
SO2 is highly correlated with NO2 and 1 other fieldsHigh correlation
NO2 is highly correlated with PM2.5 and 3 other fieldsHigh correlation
CO is highly correlated with PM2.5 and 3 other fieldsHigh correlation
O3 is highly correlated with TEMPHigh correlation
TEMP is highly correlated with O3 and 2 other fieldsHigh correlation
PRES is highly correlated with TEMP and 1 other fieldsHigh correlation
DEWP is highly correlated with TEMP and 1 other fieldsHigh correlation
REF_NO is highly correlated with yearHigh correlation
year is highly correlated with REF_NOHigh correlation
PM2.5 is highly correlated with PM10 and 2 other fieldsHigh correlation
PM10 is highly correlated with PM2.5 and 2 other fieldsHigh correlation
SO2 is highly correlated with NO2 and 1 other fieldsHigh correlation
NO2 is highly correlated with PM2.5 and 4 other fieldsHigh correlation
CO is highly correlated with PM2.5 and 3 other fieldsHigh correlation
O3 is highly correlated with NO2 and 1 other fieldsHigh correlation
TEMP is highly correlated with O3 and 2 other fieldsHigh correlation
PRES is highly correlated with TEMP and 1 other fieldsHigh correlation
DEWP is highly correlated with TEMP and 1 other fieldsHigh correlation
REF_NO is highly correlated with yearHigh correlation
year is highly correlated with REF_NOHigh correlation
PM2.5 is highly correlated with PM10 and 1 other fieldsHigh correlation
PM10 is highly correlated with PM2.5 and 1 other fieldsHigh correlation
NO2 is highly correlated with COHigh correlation
CO is highly correlated with PM2.5 and 2 other fieldsHigh correlation
TEMP is highly correlated with PRES and 1 other fieldsHigh correlation
PRES is highly correlated with TEMP and 1 other fieldsHigh correlation
DEWP is highly correlated with TEMP and 1 other fieldsHigh correlation
month is highly correlated with TEMP and 3 other fieldsHigh correlation
TEMP is highly correlated with month and 3 other fieldsHigh correlation
CO is highly correlated with PM10 and 2 other fieldsHigh correlation
PM10 is highly correlated with CO and 2 other fieldsHigh correlation
DEWP is highly correlated with month and 3 other fieldsHigh correlation
PM2.5 is highly correlated with CO and 2 other fieldsHigh correlation
NO2 is highly correlated with CO and 2 other fieldsHigh correlation
REF_NO is highly correlated with month and 4 other fieldsHigh correlation
PRES is highly correlated with month and 3 other fieldsHigh correlation
year is highly correlated with REF_NOHigh correlation
PM2.5 has 8475 (2.1%) missing values Missing
PM10 has 6222 (1.5%) missing values Missing
SO2 has 8776 (2.2%) missing values Missing
NO2 has 11859 (2.9%) missing values Missing
CO has 20261 (5.0%) missing values Missing
O3 has 13007 (3.2%) missing values Missing
RAIN is highly skewed (γ1 = 29.4402448) Skewed
REF_NO is uniformly distributed Uniform
station is uniformly distributed Uniform
hour has 16824 (4.2%) zeros Zeros
RAIN has 387119 (95.9%) zeros Zeros
WSPM has 10891 (2.7%) zeros Zeros

Reproduction

Analysis started2021-05-27 15:09:18.889390
Analysis finished2021-05-27 15:12:59.157415
Duration3 minutes and 40.27 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

REF_NO
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM

Distinct33648
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16824.5
Minimum1
Maximum33648
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2021-05-27T20:42:59.362098image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1683
Q18412.75
median16824.5
Q325236.25
95-th percentile31966
Maximum33648
Range33647
Interquartile range (IQR)16823.5

Descriptive statistics

Standard deviation9713.352953
Coefficient of variation (CV)0.5773338258
Kurtosis-1.200000002
Mean16824.5
Median Absolute Deviation (MAD)8412
Skewness0
Sum6793329312
Variance94349225.58
MonotonicityNot monotonic
2021-05-27T20:42:59.675781image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
204912
 
< 0.1%
93812
 
< 0.1%
503212
 
< 0.1%
2755912
 
< 0.1%
2551012
 
< 0.1%
3165312
 
< 0.1%
2960412
 
< 0.1%
1936312
 
< 0.1%
1731412
 
< 0.1%
2345712
 
< 0.1%
Other values (33638)403656
> 99.9%
ValueCountFrequency (%)
112
< 0.1%
212
< 0.1%
312
< 0.1%
412
< 0.1%
512
< 0.1%
612
< 0.1%
712
< 0.1%
812
< 0.1%
912
< 0.1%
1012
< 0.1%
ValueCountFrequency (%)
3364812
< 0.1%
3364712
< 0.1%
3364612
< 0.1%
3364512
< 0.1%
3364412
< 0.1%
3364312
< 0.1%
3364212
< 0.1%
3364112
< 0.1%
3364012
< 0.1%
3363912
< 0.1%

year
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
2016
105408 
2015
105120 
2014
105120 
2013
88128 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters1615104
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2013
2nd row2013
3rd row2013
4th row2013
5th row2013

Common Values

ValueCountFrequency (%)
2016105408
26.1%
2015105120
26.0%
2014105120
26.0%
201388128
21.8%

Length

2021-05-27T20:43:00.321851image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-05-27T20:43:00.504190image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
2016105408
26.1%
2015105120
26.0%
2014105120
26.0%
201388128
21.8%

Most occurring characters

ValueCountFrequency (%)
2403776
25.0%
0403776
25.0%
1403776
25.0%
6105408
 
6.5%
4105120
 
6.5%
5105120
 
6.5%
388128
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1615104
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2403776
25.0%
0403776
25.0%
1403776
25.0%
6105408
 
6.5%
4105120
 
6.5%
5105120
 
6.5%
388128
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
Common1615104
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2403776
25.0%
0403776
25.0%
1403776
25.0%
6105408
 
6.5%
4105120
 
6.5%
5105120
 
6.5%
388128
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1615104
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2403776
25.0%
0403776
25.0%
1403776
25.0%
6105408
 
6.5%
4105120
 
6.5%
5105120
 
6.5%
388128
 
5.5%

month
Real number (ℝ≥0)

HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.735378031
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2021-05-27T20:43:00.721941image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.356479072
Coefficient of variation (CV)0.4983356623
Kurtosis-1.157296025
Mean6.735378031
Median Absolute Deviation (MAD)3
Skewness-0.0532691034
Sum2719584
Variance11.26595176
MonotonicityNot monotonic
2021-05-27T20:43:00.968759image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
335712
8.8%
535712
8.8%
735712
8.8%
835712
8.8%
1035712
8.8%
1235712
8.8%
434560
8.6%
634560
8.6%
934560
8.6%
1134560
8.6%
Other values (2)51264
12.7%
ValueCountFrequency (%)
126784
6.6%
224480
6.1%
335712
8.8%
434560
8.6%
535712
8.8%
634560
8.6%
735712
8.8%
835712
8.8%
934560
8.6%
1035712
8.8%
ValueCountFrequency (%)
1235712
8.8%
1134560
8.6%
1035712
8.8%
934560
8.6%
835712
8.8%
735712
8.8%
634560
8.6%
535712
8.8%
434560
8.6%
335712
8.8%

day
Real number (ℝ≥0)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.74821683
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2021-05-27T20:43:01.258264image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.808891484
Coefficient of variation (CV)0.5593580262
Kurtosis-1.195325155
Mean15.74821683
Median Absolute Deviation (MAD)8
Skewness0.005682826695
Sum6358752
Variance77.59656917
MonotonicityNot monotonic
2021-05-27T20:43:01.513862image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
113248
 
3.3%
213248
 
3.3%
2813248
 
3.3%
2713248
 
3.3%
2613248
 
3.3%
2513248
 
3.3%
2413248
 
3.3%
2313248
 
3.3%
2213248
 
3.3%
2113248
 
3.3%
Other values (21)271296
67.2%
ValueCountFrequency (%)
113248
3.3%
213248
3.3%
313248
3.3%
413248
3.3%
513248
3.3%
613248
3.3%
713248
3.3%
813248
3.3%
913248
3.3%
1013248
3.3%
ValueCountFrequency (%)
317776
1.9%
3012384
3.1%
2912672
3.1%
2813248
3.3%
2713248
3.3%
2613248
3.3%
2513248
3.3%
2413248
3.3%
2313248
3.3%
2213248
3.3%

hour
Real number (ℝ≥0)

ZEROS

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.5
Minimum0
Maximum23
Zeros16824
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2021-05-27T20:43:01.803797image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15.75
median11.5
Q317.25
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation6.922195124
Coefficient of variation (CV)0.6019300108
Kurtosis-1.204173965
Mean11.5
Median Absolute Deviation (MAD)6
Skewness0
Sum4643424
Variance47.91678534
MonotonicityNot monotonic
2021-05-27T20:43:02.045357image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
016824
 
4.2%
116824
 
4.2%
2216824
 
4.2%
2116824
 
4.2%
2016824
 
4.2%
1916824
 
4.2%
1816824
 
4.2%
1716824
 
4.2%
1616824
 
4.2%
1516824
 
4.2%
Other values (14)235536
58.3%
ValueCountFrequency (%)
016824
4.2%
116824
4.2%
216824
4.2%
316824
4.2%
416824
4.2%
516824
4.2%
616824
4.2%
716824
4.2%
816824
4.2%
916824
4.2%
ValueCountFrequency (%)
2316824
4.2%
2216824
4.2%
2116824
4.2%
2016824
4.2%
1916824
4.2%
1816824
4.2%
1716824
4.2%
1616824
4.2%
1516824
4.2%
1416824
4.2%

PM2.5
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct866
Distinct (%)0.2%
Missing8475
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean79.24827511
Minimum2
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2021-05-27T20:43:02.384884image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile6
Q121
median55
Q3110
95-th percentile238
Maximum999
Range997
Interquartile range (IQR)89

Descriptive statistics

Standard deviation79.14670837
Coefficient of variation (CV)0.9987183728
Kurtosis5.728756991
Mean79.24827511
Median Absolute Deviation (MAD)39
Skewness1.974286544
Sum31326922.4
Variance6264.201445
MonotonicityNot monotonic
2021-05-27T20:43:02.675119image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38354
 
2.1%
106609
 
1.6%
116418
 
1.6%
96374
 
1.6%
126346
 
1.6%
86333
 
1.6%
135830
 
1.4%
145765
 
1.4%
75742
 
1.4%
65116
 
1.3%
Other values (856)332414
82.3%
(Missing)8475
 
2.1%
ValueCountFrequency (%)
27
 
< 0.1%
38354
2.1%
43221
 
0.8%
4.32
 
< 0.1%
4.41
 
< 0.1%
4.61
 
< 0.1%
53984
1.0%
65116
1.3%
75742
1.4%
7.21
 
< 0.1%
ValueCountFrequency (%)
9991
< 0.1%
9571
< 0.1%
9411
< 0.1%
8981
< 0.1%
8821
< 0.1%
8811
< 0.1%
8571
< 0.1%
8441
< 0.1%
8261
< 0.1%
8211
< 0.1%

PM10
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1048
Distinct (%)0.3%
Missing6222
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean104.3278973
Minimum2
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2021-05-27T20:43:02.972607image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile10
Q136
median83
Q3145
95-th percentile277
Maximum999
Range997
Interquartile range (IQR)109

Descriptive statistics

Standard deviation90.13639956
Coefficient of variation (CV)0.8639721671
Kurtosis5.737014891
Mean104.3278973
Median Absolute Deviation (MAD)52
Skewness1.816481632
Sum41475972.9
Variance8124.570525
MonotonicityNot monotonic
2021-05-27T20:43:03.268657image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
64712
 
1.2%
53547
 
0.9%
183523
 
0.9%
143493
 
0.9%
163405
 
0.8%
173383
 
0.8%
133349
 
0.8%
203336
 
0.8%
243240
 
0.8%
213229
 
0.8%
Other values (1038)362337
89.7%
(Missing)6222
 
1.5%
ValueCountFrequency (%)
2103
 
< 0.1%
3719
 
0.2%
4264
 
0.1%
53547
0.9%
5.42
 
< 0.1%
5.61
 
< 0.1%
64712
1.2%
6.41
 
< 0.1%
6.61
 
< 0.1%
72245
0.6%
ValueCountFrequency (%)
9993
< 0.1%
9951
 
< 0.1%
9931
 
< 0.1%
9921
 
< 0.1%
9911
 
< 0.1%
9881
 
< 0.1%
9871
 
< 0.1%
9861
 
< 0.1%
9841
 
< 0.1%
9831
 
< 0.1%

SO2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct685
Distinct (%)0.2%
Missing8776
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean15.73305999
Minimum0.2856
Maximum500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2021-05-27T20:43:03.532725image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.2856
5-th percentile2
Q12
median7
Q319
95-th percentile61
Maximum500
Range499.7144
Interquartile range (IQR)17

Descriptive statistics

Standard deviation21.73945549
Coefficient of variation (CV)1.381769058
Kurtosis14.00498947
Mean15.73305999
Median Absolute Deviation (MAD)5
Skewness3.007737087
Sum6214558.695
Variance472.6039248
MonotonicityNot monotonic
2021-05-27T20:43:03.829399image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
297027
24.0%
331771
 
7.9%
420810
 
5.2%
517091
 
4.2%
615762
 
3.9%
713639
 
3.4%
812722
 
3.2%
910952
 
2.7%
1010096
 
2.5%
118863
 
2.2%
Other values (675)156267
38.7%
(Missing)8776
 
2.2%
ValueCountFrequency (%)
0.285689
 
< 0.1%
0.571270
 
< 0.1%
0.856872
 
< 0.1%
13221
 
0.8%
1.142484
 
< 0.1%
1.42894
 
< 0.1%
1.713683
 
< 0.1%
1.9992110
 
< 0.1%
297027
24.0%
2.11
 
< 0.1%
ValueCountFrequency (%)
5003
< 0.1%
4111
 
< 0.1%
3411
 
< 0.1%
3151
 
< 0.1%
3141
 
< 0.1%
3101
 
< 0.1%
2991
 
< 0.1%
2821
 
< 0.1%
2781
 
< 0.1%
2771
 
< 0.1%

NO2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1209
Distinct (%)0.3%
Missing11859
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean50.35278459
Minimum1.0265
Maximum290
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2021-05-27T20:43:04.110874image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1.0265
5-th percentile8
Q123
median43
Q371
95-th percentile116
Maximum290
Range288.9735
Interquartile range (IQR)48

Descriptive statistics

Standard deviation34.77190967
Coefficient of variation (CV)0.6905657741
Kurtosis1.211420478
Mean50.35278459
Median Absolute Deviation (MAD)23
Skewness1.052701359
Sum19734112.28
Variance1209.085702
MonotonicityNot monotonic
2021-05-27T20:43:05.615206image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
165572
 
1.4%
225556
 
1.4%
205523
 
1.4%
175467
 
1.4%
185441
 
1.3%
265420
 
1.3%
215416
 
1.3%
195368
 
1.3%
145366
 
1.3%
245358
 
1.3%
Other values (1199)337430
83.6%
(Missing)11859
 
2.9%
ValueCountFrequency (%)
1.02653
 
< 0.1%
1.23182
 
< 0.1%
1.43712
 
< 0.1%
1.64243
 
< 0.1%
1.84771
 
< 0.1%
24364
1.1%
2.0531
 
< 0.1%
2.25833
 
< 0.1%
2.46361
 
< 0.1%
2.66892
 
< 0.1%
ValueCountFrequency (%)
2901
< 0.1%
2851
< 0.1%
2801
< 0.1%
2772
< 0.1%
2731
< 0.1%
2701
< 0.1%
2691
< 0.1%
2651
< 0.1%
2641
< 0.1%
2632
< 0.1%

CO
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct132
Distinct (%)< 0.1%
Missing20261
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean1214.843339
Minimum100
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2021-05-27T20:43:05.960165image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile200
Q1500
median900
Q31500
95-th percentile3400
Maximum10000
Range9900
Interquartile range (IQR)1000

Descriptive statistics

Standard deviation1124.285676
Coefficient of variation (CV)0.925457333
Kurtosis9.450258696
Mean1214.843339
Median Absolute Deviation (MAD)500
Skewness2.56066181
Sum465910643
Variance1264018.282
MonotonicityNot monotonic
2021-05-27T20:43:06.264273image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30030662
 
7.6%
40029849
 
7.4%
50028043
 
6.9%
60027189
 
6.7%
70025720
 
6.4%
80022728
 
5.6%
90020655
 
5.1%
100019026
 
4.7%
20017370
 
4.3%
110017009
 
4.2%
Other values (122)145264
36.0%
(Missing)20261
 
5.0%
ValueCountFrequency (%)
1005091
 
1.3%
1501
 
< 0.1%
20017370
4.3%
30030662
7.6%
3501
 
< 0.1%
40029849
7.4%
50028043
6.9%
60027189
6.7%
70025720
6.4%
80022728
5.6%
ValueCountFrequency (%)
1000051
< 0.1%
990025
< 0.1%
980024
< 0.1%
970023
< 0.1%
960023
< 0.1%
950022
< 0.1%
940025
< 0.1%
930031
< 0.1%
920031
< 0.1%
910031
< 0.1%

O3
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1597
Distinct (%)0.4%
Missing13007
Missing (%)3.2%
Infinite0
Infinite (%)0.0%
Mean58.11932675
Minimum0.2142
Maximum1071
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2021-05-27T20:43:06.591244image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.2142
5-th percentile2
Q111
median45
Q383
95-th percentile180
Maximum1071
Range1070.7858
Interquartile range (IQR)72

Descriptive statistics

Standard deviation57.37596606
Coefficient of variation (CV)0.9872097505
Kurtosis6.074069635
Mean58.11932675
Median Absolute Deviation (MAD)36
Skewness1.635163683
Sum22711231.2
Variance3292.001482
MonotonicityNot monotonic
2021-05-27T20:43:06.936758image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
240544
 
10.0%
38245
 
2.0%
47636
 
1.9%
16878
 
1.7%
56129
 
1.5%
65641
 
1.4%
84796
 
1.2%
74642
 
1.1%
103940
 
1.0%
93936
 
1.0%
Other values (1587)298382
73.9%
(Missing)13007
 
3.2%
ValueCountFrequency (%)
0.2142134
 
< 0.1%
0.4284119
 
< 0.1%
0.6426118
 
< 0.1%
0.8568120
 
< 0.1%
16878
1.7%
1.071138
 
< 0.1%
1.2852147
 
< 0.1%
1.4994166
 
< 0.1%
1.7136125
 
< 0.1%
1.9278147
 
< 0.1%
ValueCountFrequency (%)
107114
< 0.1%
10501
 
< 0.1%
10261
 
< 0.1%
6741
 
< 0.1%
6731
 
< 0.1%
5005
 
< 0.1%
4501
 
< 0.1%
4441
 
< 0.1%
4321
 
< 0.1%
4291
 
< 0.1%

TEMP
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1180
Distinct (%)0.3%
Missing264
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean14.08889947
Minimum-19.9
Maximum41.6
Zeros2642
Zeros (%)0.7%
Negative55474
Negative (%)13.7%
Memory size3.1 MiB
2021-05-27T20:43:07.267588image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-19.9
5-th percentile-4
Q14
median15.4
Q323.5
95-th percentile30.7
Maximum41.6
Range61.5
Interquartile range (IQR)19.5

Descriptive statistics

Standard deviation11.30353352
Coefficient of variation (CV)0.802300672
Kurtosis-1.087420248
Mean14.08889947
Median Absolute Deviation (MAD)9.4
Skewness-0.1686978359
Sum5685040.005
Variance127.76987
MonotonicityNot monotonic
2021-05-27T20:43:07.615634image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33342
 
0.8%
12796
 
0.7%
02642
 
0.7%
22556
 
0.6%
-12436
 
0.6%
-22293
 
0.6%
-41844
 
0.5%
41772
 
0.4%
51680
 
0.4%
-51633
 
0.4%
Other values (1170)380518
94.2%
ValueCountFrequency (%)
-19.91
< 0.1%
-19.71
< 0.1%
-19.51
< 0.1%
-18.91
< 0.1%
-18.71
< 0.1%
-18.51
< 0.1%
-18.11
< 0.1%
-17.91
< 0.1%
-17.41
< 0.1%
-17.31
< 0.1%
ValueCountFrequency (%)
41.61
 
< 0.1%
41.42
 
< 0.1%
41.13
 
< 0.1%
412
 
< 0.1%
40.91
 
< 0.1%
40.62
 
< 0.1%
40.58
< 0.1%
40.43
 
< 0.1%
40.34
< 0.1%
40.22
 
< 0.1%

PRES
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct675
Distinct (%)0.2%
Missing265
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1010.282534
Minimum982.4
Maximum1042.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2021-05-27T20:43:07.967211image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum982.4
5-th percentile994.6
Q11002
median1009.8
Q31018.3
95-th percentile1027.4
Maximum1042.8
Range60.4
Interquartile range (IQR)16.3

Descriptive statistics

Standard deviation10.35677799
Coefficient of variation (CV)0.01025136795
Kurtosis-0.7829195448
Mean1010.282534
Median Absolute Deviation (MAD)8.2
Skewness0.1519478448
Sum407660115.7
Variance107.2628503
MonotonicityNot monotonic
2021-05-27T20:43:08.271314image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10192712
 
0.7%
10182695
 
0.7%
10212691
 
0.7%
10152602
 
0.6%
10232596
 
0.6%
10202570
 
0.6%
10172554
 
0.6%
10162528
 
0.6%
10222474
 
0.6%
10242455
 
0.6%
Other values (665)377634
93.5%
ValueCountFrequency (%)
982.42
 
< 0.1%
982.72
 
< 0.1%
982.83
< 0.1%
982.92
 
< 0.1%
9834
< 0.1%
983.24
< 0.1%
983.33
< 0.1%
983.42
 
< 0.1%
983.56
< 0.1%
983.64
< 0.1%
ValueCountFrequency (%)
1042.82
 
< 0.1%
1042.41
 
< 0.1%
1042.32
 
< 0.1%
1042.21
 
< 0.1%
104211
< 0.1%
1041.88
< 0.1%
1041.71
 
< 0.1%
1041.67
< 0.1%
1041.52
 
< 0.1%
1041.48
< 0.1%

DEWP
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct645
Distinct (%)0.2%
Missing269
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean3.157291447
Minimum-43.4
Maximum29.1
Zeros828
Zeros (%)0.2%
Negative168595
Negative (%)41.8%
Memory size3.1 MiB
2021-05-27T20:43:08.585904image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-43.4
5-th percentile-19.4
Q1-8
median4.2
Q315.5
95-th percentile22.2
Maximum29.1
Range72.5
Interquartile range (IQR)23.5

Descriptive statistics

Standard deviation13.61727272
Coefficient of variation (CV)4.312960315
Kurtosis-1.078189528
Mean3.157291447
Median Absolute Deviation (MAD)11.6
Skewness-0.2500222557
Sum1273989.2
Variance185.4301162
MonotonicityNot monotonic
2021-05-27T20:43:08.942539image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.61559
 
0.4%
171519
 
0.4%
17.21490
 
0.4%
16.81483
 
0.4%
17.31455
 
0.4%
17.11445
 
0.4%
17.81440
 
0.4%
16.21429
 
0.4%
18.21426
 
0.4%
17.51409
 
0.3%
Other values (635)388852
96.3%
ValueCountFrequency (%)
-43.41
 
< 0.1%
-361
 
< 0.1%
-35.71
 
< 0.1%
-35.51
 
< 0.1%
-35.37
< 0.1%
-35.19
< 0.1%
-356
< 0.1%
-34.92
 
< 0.1%
-34.87
< 0.1%
-34.62
 
< 0.1%
ValueCountFrequency (%)
29.12
 
< 0.1%
291
 
< 0.1%
28.810
< 0.1%
28.712
< 0.1%
28.62
 
< 0.1%
28.512
< 0.1%
28.414
< 0.1%
28.314
< 0.1%
28.29
< 0.1%
28.19
< 0.1%

RAIN
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct253
Distinct (%)0.1%
Missing261
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.06705178246
Minimum0
Maximum72.5
Zeros387119
Zeros (%)95.9%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2021-05-27T20:43:09.322406image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum72.5
Range72.5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.8378448668
Coefficient of variation (CV)12.49548984
Kurtosis1291.908304
Mean0.06705178246
Median Absolute Deviation (MAD)0
Skewness29.4402448
Sum27056.4
Variance0.7019840208
MonotonicityNot monotonic
2021-05-27T20:43:09.738515image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0387119
95.9%
0.13689
 
0.9%
0.21823
 
0.5%
0.31374
 
0.3%
0.4885
 
0.2%
0.5847
 
0.2%
0.6698
 
0.2%
0.7585
 
0.1%
0.9502
 
0.1%
0.8482
 
0.1%
Other values (243)5511
 
1.4%
ValueCountFrequency (%)
0387119
95.9%
0.13689
 
0.9%
0.21823
 
0.5%
0.31374
 
0.3%
0.4885
 
0.2%
0.5847
 
0.2%
0.6698
 
0.2%
0.7585
 
0.1%
0.8482
 
0.1%
0.9502
 
0.1%
ValueCountFrequency (%)
72.53
< 0.1%
52.12
 
< 0.1%
47.71
 
< 0.1%
46.46
< 0.1%
45.92
 
< 0.1%
41.91
 
< 0.1%
40.73
< 0.1%
391
 
< 0.1%
38.91
 
< 0.1%
37.42
 
< 0.1%

wd
Categorical

Distinct16
Distinct (%)< 0.1%
Missing1389
Missing (%)0.3%
Memory size3.1 MiB
NE
40049 
ENE
33262 
N
29973 
NW
29587 
E
29168 
Other values (11)
240348 

Length

Max length3
Median length2
Mean length2.238176184
Min length1

Characters and Unicode

Total characters900613
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNNW
2nd rowN
3rd rowNNW
4th rowNW
5th rowN

Common Values

ValueCountFrequency (%)
NE40049
 
9.9%
ENE33262
 
8.2%
N29973
 
7.4%
NW29587
 
7.3%
E29168
 
7.2%
NNE27247
 
6.7%
SW27083
 
6.7%
NNW24167
 
6.0%
WNW23815
 
5.9%
ESE23691
 
5.9%
Other values (6)114345
28.3%

Length

2021-05-27T20:43:10.465437image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ne40049
 
10.0%
ene33262
 
8.3%
n29973
 
7.4%
nw29587
 
7.4%
e29168
 
7.2%
nne27247
 
6.8%
sw27083
 
6.7%
nnw24167
 
6.0%
wnw23815
 
5.9%
ese23691
 
5.9%
Other values (6)114345
28.4%

Most occurring characters

ValueCountFrequency (%)
N259514
28.8%
E246725
27.4%
W207045
23.0%
S187329
20.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter900613
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N259514
28.8%
E246725
27.4%
W207045
23.0%
S187329
20.8%

Most occurring scripts

ValueCountFrequency (%)
Latin900613
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N259514
28.8%
E246725
27.4%
W207045
23.0%
S187329
20.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII900613
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N259514
28.8%
E246725
27.4%
W207045
23.0%
S187329
20.8%

WSPM
Real number (ℝ≥0)

ZEROS

Distinct115
Distinct (%)< 0.1%
Missing238
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1.718379682
Minimum0
Maximum13.2
Zeros10891
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2021-05-27T20:43:10.808005image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3
Q10.9
median1.4
Q32.2
95-th percentile4.2
Maximum13.2
Range13.2
Interquartile range (IQR)1.3

Descriptive statistics

Standard deviation1.237964878
Coefficient of variation (CV)0.7204256958
Kurtosis3.691546729
Mean1.718379682
Median Absolute Deviation (MAD)0.6
Skewness1.625270041
Sum693431.5
Variance1.532557039
MonotonicityNot monotonic
2021-05-27T20:43:11.183842image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.121486
 
5.3%
121370
 
5.3%
1.221228
 
5.3%
0.920237
 
5.0%
1.319640
 
4.9%
0.818585
 
4.6%
1.417776
 
4.4%
0.716969
 
4.2%
1.516273
 
4.0%
1.615098
 
3.7%
Other values (105)214876
53.2%
ValueCountFrequency (%)
010891
2.7%
0.14175
 
1.0%
0.24378
 
1.1%
0.32673
 
0.7%
0.47154
 
1.8%
0.510842
2.7%
0.613881
3.4%
0.716969
4.2%
0.818585
4.6%
0.920237
5.0%
ValueCountFrequency (%)
13.21
 
< 0.1%
12.91
 
< 0.1%
12.81
 
< 0.1%
11.81
 
< 0.1%
11.71
 
< 0.1%
11.23
< 0.1%
111
 
< 0.1%
10.93
< 0.1%
10.71
 
< 0.1%
10.53
< 0.1%

station
Categorical

UNIFORM

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
Wanshouxigong
33648 
Gucheng
33648 
Nongzhanguan
33648 
Tiantan
33648 
Shunyi
33648 
Other values (7)
235536 

Length

Max length13
Median length7.5
Mean length8.416666667
Min length6

Characters and Unicode

Total characters3398448
Distinct characters26
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAotizhongxin
2nd rowAotizhongxin
3rd rowAotizhongxin
4th rowAotizhongxin
5th rowAotizhongxin

Common Values

ValueCountFrequency (%)
Wanshouxigong33648
8.3%
Gucheng33648
8.3%
Nongzhanguan33648
8.3%
Tiantan33648
8.3%
Shunyi33648
8.3%
Dingling33648
8.3%
Guanyuan33648
8.3%
Dongsi33648
8.3%
Wanliu33648
8.3%
Huairou33648
8.3%
Other values (2)67296
16.7%

Length

2021-05-27T20:43:11.785052image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
guanyuan33648
8.3%
dongsi33648
8.3%
dingling33648
8.3%
tiantan33648
8.3%
gucheng33648
8.3%
nongzhanguan33648
8.3%
wanliu33648
8.3%
wanshouxigong33648
8.3%
aotizhongxin33648
8.3%
changping33648
8.3%
Other values (2)67296
16.7%

Most occurring characters

ValueCountFrequency (%)
n639312
18.8%
i370128
10.9%
g370128
10.9%
a336480
9.9%
u302832
8.9%
o235536
 
6.9%
h201888
 
5.9%
t67296
 
2.0%
z67296
 
2.0%
x67296
 
2.0%
Other values (16)740256
21.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2994672
88.1%
Uppercase Letter403776
 
11.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n639312
21.3%
i370128
12.4%
g370128
12.4%
a336480
11.2%
u302832
10.1%
o235536
 
7.9%
h201888
 
6.7%
t67296
 
2.2%
z67296
 
2.2%
x67296
 
2.2%
Other values (7)336480
11.2%
Uppercase Letter
ValueCountFrequency (%)
D67296
16.7%
G67296
16.7%
W67296
16.7%
A33648
8.3%
C33648
8.3%
H33648
8.3%
N33648
8.3%
S33648
8.3%
T33648
8.3%

Most occurring scripts

ValueCountFrequency (%)
Latin3398448
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n639312
18.8%
i370128
10.9%
g370128
10.9%
a336480
9.9%
u302832
8.9%
o235536
 
6.9%
h201888
 
5.9%
t67296
 
2.0%
z67296
 
2.0%
x67296
 
2.0%
Other values (16)740256
21.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII3398448
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n639312
18.8%
i370128
10.9%
g370128
10.9%
a336480
9.9%
u302832
8.9%
o235536
 
6.9%
h201888
 
5.9%
t67296
 
2.0%
z67296
 
2.0%
x67296
 
2.0%
Other values (16)740256
21.8%

Interactions

2021-05-27T20:40:46.613317image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T20:40:47.254918image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T20:40:47.818708image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T20:40:48.425070image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T20:40:49.004062image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T20:40:49.582051image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T20:40:50.125393image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-05-27T20:42:42.518953image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T20:42:43.148796image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T20:42:43.782489image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T20:42:44.341396image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T20:42:44.920825image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T20:42:45.481470image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T20:42:46.017617image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T20:42:46.561777image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T20:42:47.097886image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T20:42:47.650966image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T20:42:48.203021image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T20:42:48.756823image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T20:42:49.292721image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T20:42:49.844887image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T20:42:50.406532image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T20:42:51.037161image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-05-27T20:42:51.681459image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-05-27T20:43:12.117442image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-05-27T20:43:12.776056image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-05-27T20:43:13.384615image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-05-27T20:43:14.068413image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-05-27T20:43:14.702153image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-05-27T20:42:52.452480image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-05-27T20:42:54.144865image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-05-27T20:42:57.174366image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-05-27T20:42:58.174881image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

REF_NOyearmonthdayhourPM2.5PM10SO2NO2COO3TEMPPRESDEWPRAINwdWSPMstation
0120133104.000004.000004.000007.00000300.0000077.00000-0.700001023.00000-18.800000.00000NNW4.40000Aotizhongxin
1220133118.000008.000004.000007.00000300.0000077.00000-1.100001023.20000-18.200000.00000N4.70000Aotizhongxin
2320133127.000007.000005.0000010.00000300.0000073.00000-1.100001023.50000-18.200000.00000NNW5.60000Aotizhongxin
3420133136.000006.0000011.0000011.00000300.0000072.00000-1.400001024.50000-19.400000.00000NW3.10000Aotizhongxin
4520133143.000003.0000012.0000012.00000300.0000072.00000-2.000001025.20000-19.500000.00000N2.00000Aotizhongxin
5620133155.000005.0000018.0000018.00000400.0000066.00000-2.200001025.60000-19.600000.00000N3.70000Aotizhongxin
6720133163.000003.0000018.0000032.00000500.0000050.00000-2.600001026.50000-19.100000.00000NNE2.50000Aotizhongxin
7820133173.000006.0000019.0000041.00000500.0000043.00000-1.600001027.40000-19.100000.00000NNW3.80000Aotizhongxin
8920133183.000006.0000016.0000043.00000500.0000045.000000.100001028.30000-19.200000.00000NNW4.10000Aotizhongxin
91020133193.000008.0000012.0000028.00000400.0000059.000001.200001028.50000-19.300000.00000N2.60000Aotizhongxin

Last rows

REF_NOyearmonthdayhourPM2.5PM10SO2NO2COO3TEMPPRESDEWPRAINwdWSPMstation
403766336392016123114399.00000412.0000031.00000198.000004900.000006.000003.800001021.90000-8.900000.00000SSE1.00000Wanshouxigong
403767336402016123115449.00000524.0000030.00000217.000005600.000008.000003.900001021.50000-6.100000.00000S1.40000Wanshouxigong
403768336412016123116440.00000440.0000026.00000200.000004700.000006.000002.800001021.50000-6.600000.00000SSE0.70000Wanshouxigong
403769336422016123117378.00000378.0000020.00000171.000003800.000004.000001.200001021.40000-5.500000.00000SSE1.10000Wanshouxigong
403770336432016123118392.00000458.0000014.00000160.000003900.000003.00000-1.300001021.90000-6.500000.00000S0.60000Wanshouxigong
403771336442016123119449.00000487.0000010.00000153.000004500.000004.00000-1.900001022.00000-6.100000.00000ESE0.90000Wanshouxigong
403772336452016123120460.00000492.0000012.00000146.000004100.000004.00000-2.500001022.40000-5.500000.00000ENE0.70000Wanshouxigong
403773336462016123121463.00000498.0000012.00000141.000004400.000005.00000-3.000001022.10000-5.300000.00000E0.90000Wanshouxigong
403774336472016123122493.00000537.0000012.00000124.000005000.000008.00000-3.000001022.70000-5.000000.00000SW0.10000Wanshouxigong
403775336482016123123464.00000490.000008.00000111.000005400.000007.00000-4.000001022.60000-5.700000.00000ENE0.90000Wanshouxigong